J. Phys. Ther. Sci. 28: 626–631, 2016
The Journal of Physical Therapy Science Original Article
Kinematic characteristics of the lower extremity during a simulated skiing exercise in healthy participants Hyo Taek Lee, PhD1), Hyo Lyun Roh, PhD, PT2), Yoon Sang K im, PhD1)* 1) Human-centered
Interaction Laboratory, Department of Computer Science and Engineering, Korea University of Technology and Education: 1600 Chunjeol-ro, Cheonan 330-708, Republic of Korea 2) Department of Physical Therapy, Kangwon National University, Republic of Korea
Abstract. [Purpose] Currently, various simulators are produced and used for athlete’s exercise, rehabilitation, and training. In this study, we analyzed the kinematic factors of sectional and total movements in healthy participants by providing group-dependent information during simulated exercise. [Subjects and Methods] Participants in this study included 26 male adults (non-experts and experts); experts held a certificate issued by the Korea Ski Instructors Association. The elapsed times in each phase, the difference in the lower extremity angles, and muscle activity were computed through analysis of kinematic factors. [Results] We observed that motions in the experts took shorter time to perform than that in non-experts, and showed larger variation of lower limb joint angle in most events during simulated skiing. There were also significant group-dependent differences in the peak and mean EMG values during simulated skiing. [Conclusion] A non-expert’s posture leads to enhanced muscle activity to keep the lower body in balance. We suggest the following training guideline: initially, non-experts should maintain appropriate range of motion with lower-intensity exercise to improve muscle endurance. It can be useful in providing preliminary data for future training and rehabilitation studies, as well as improvements in muscle strength and balance. Key words: Lower extremity, Muscle activity, Simulated skiing (This article was submitted Sep. 30, 2015, and was accepted Nov. 19, 2015)
INTRODUCTION High postural performance in normal and challenging conditions is crucial not only for many daily activities but also for attaining excellence and avoiding injuries in many sports. In alpine skiing especially, postural exercises are introduced in the training programs of expert skiers. Training for postural control can improve postural performance in healthy and non-injured athletes1). Specific indoor balance training is beneficial in learning alpine skiing2) and muscle strength training for ordinary people who have restrictions on outdoor activities. To allow people to enjoy sports such as skiing conveniently in a confined space during non-winter seasons, ski activities, through a ski simulator, provide a way to efficiently practice ski-turn motor performance4). Recently, research has focused on the development of ski simulators and exercise using them. Additionally, studies on the utilization of ski simulators to prevent injury are in progress. Lee et al.3) reported that training with a ski simulator contributes to body movement as well as strengthening the hamstrings, which helps prevent sports-related injuries. Muller et al.4) developed a specific training device called ski power simulator based on a kinetic and kinematic analysis of alpine slalom performed by senior and junior Austrian national teams. Panizzolo et al.5) have focused on 3-D motion analysis systems with reflective surface markers. In addition to this, Panizzolo et al.5) have used ski simulators to study equipment
*Corresponding author. Yoon Sang Kim (E-mail: [email protected]) ©2016 The Society of Physical Therapy Science. Published by IPEC Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License .
characteristics and exercise methods and compared their measurements with those measured in similar conditions while skiing on snow. Results of their study suggest that the two devices (skier’s edge and skimagic treadmill) are not effectively applicable for strength training, but the pattern of EMG activity was closer to real snow skiing. Currently, various simulator products are produced by many organizations, and ski simulators are used for personal and public rehabilitation as well as training. Furthermore, differences between individuals exist depending on the level of ski technique and the type of simulator. If the purpose of the ski simulator is to provide effects similar to actual skiing, it should encourage accurate posture and provide kinematic information not only for athletes but also for many daily and recreational skiers. At a time when Korea and China are preparing to host the 2018 and 2022 Winter Olympic Games, interest in skiing is growing. In addition to an increase in the skiing population in Asian countries, the rapid increase in the absolute number of injuries incurred while skiing has made the sport a major issue of interest in the area of sport injuries3). Being able to easily partake in a sports activity such as skiing in a confined area is thought to be useful in providing preliminary data for future training and rehabilitation studies, as well as improvements in muscle strength and balance. The primary aim of this study was to capture the kinematic characteristics in lower extremity range of motion (ROM) between two groups (non-expert and expert) and the effect of lower limb muscle activity during ski simulator exercise. A secondary aim of this study was to use the information collected to help maintain a safer and more accurate posture and efficiently use the joint’s range of motion and agonistic muscles in a ski simulator exercise.
SUBJECTS AND METHODS Twenty-six male adults (non-expert 13, expert 13) with no previous/current foot injury participated in this study. Subjects with any pathologies that could have affected the research and its results were excluded from the study6). Expert participants held a certificate issued by the Korea Ski Instructors Association and knew how to use a ski simulator. The study sample size was calculated using G*Power Version 3.17). With a 0.05 significance level and 0.8 power, a sample size of 12 participants per groups was calculated. The experiment was approved by the Pukyong National University Institutional Review Board. This experiment was conducted after its purpose and methods were sufficiently explained to the participants and they had consented to participate in this experiment. This experiment was conducted at a motion analysis laboratory in B City. The ski simulators (Pro ski simulator; Slovenia) were fixed onto a flat surface. For accurate results, sufficient time was given to each subject to practice, and then, the actual skiing footage was filmed for 30 seconds. Subjects wore tight pants in order to reduce discomfort and errors while collecting data. They held a pole in each hand; the middle intensities in the ski simulator were used in this study. In order to analyze the kinematic data dependent on each group, 5 infrared cameras (Visol, Korea) were used to record the participants’ performance of each movement. Motion data were sampled at 200 Hz. In order to set the reference frame, a calibration was conducted using a control box and surface markers were placed on 19 areas of the lower limbs to obtain the location coordinates of segments and joints (Table 1). Prior to testing, a relaxed standing calibration trial was captured first. EMG signals of 4 muscles of interest were selected on both legs, and skin preparation and electrode placement over the intended muscles were performed in accordance with the SENIAM8) concerted protocol. In order to prevent the noise due to extension lead between the surface electrodes and measuring instrument, the extension lead was fixed using tape. We measured the maximum voluntary isometric contraction (MVIC) of the individual muscles and the position of the surface electrode was attached to the rectus femoris, biceps femoris, tibialis anterior and gastrocnemius. The sampling frequency was established at 1,024 Hz. Motion analysis (Visol; Korea) and electromyography analysis (Laxtha; Korea) programs were used for data processing. For the data, the 41 control point system applied in calibration was used. The actual spatial coordinates were obtained and 3D coordinates of the human body were computed. The model of the human body was defined as a rigid body system. To remove digitizing errors and noise, the source data was filtered through the butterworth low pass filter. The cut-off frequency was set to 6 Hz and the 2D coordinates obtained from each digital camcorder were confirmed through the interpolation by cubic spline function. 3D coordinate calculations were analyzed using the DLT (Direct Linear Translation) method in kwon3d software. The computation of data were categorized into kinematic factors regarding muscle activities and then analyzed. The electromyography data were filtered through a 10 Hz high pass filter using Telescan (Laxtha) to minimize the noise afterwards. The obtained root mean square (RMS) value was standardized into a MVIC value and the %MVIC value was calculated. For the analysis of motion on the ski simulator, the points in time when the participants were at the center, when they were at the right peak and when they returned to the center were defined as Event 1, Event 2, and Event 3, respectively (Fig. 1). For an easy analysis of the movement in ski simulators, the signal recorded on the video data was used to set the analysis segments. In the event that the video lasted over 30 seconds, the best movement was chosen and classified into 3 events and 2 phases. Using these methods, the elapsed times in each phase, the difference in the lower extremity angles, and muscle activity were computed through analysis of kinematic factors. Participants’ performance was statistically analyzed using SPSS version 23.0. An independent samples t-test was conducted to examine changes in the lower joint motions, elapsed times, and muscle activity between the two groups. Differences were considered statistically significant at a p-value of less than 0.05.
627
Table 1. Surface markers of the lower limb joints Body model 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Utilized data Mid posterior superior iliac spine Right anterior superior iliac spine Left anterior superior iliac spine Right lateral thigh Left lateral thigh Right medial epicondyle Left medial epicondyle Right lateral epicondyle Left lateral epicondyle Right lateral shank Left lateral shank Right lateral malleolus Left lateral malleolus Right medial malleolus Left medial malleolus Right heel Left heel Right toe Left toe
Fig. 1. Event and phase of simulator exercise
RESULTS Observation of the elapsed times in each phase showed that non-experts spent 0.41 seconds in phase 1 and 0.40 seconds in phase 2. In the expert group, 0.35 seconds and 0.38 seconds were spent in phase 1 and 2, respectively. The total time spent was 0.82 seconds for non-experts, which was significantly longer than the 0.73 seconds observed in the experts (p
The Journal of Physical Therapy Science Original Article
Kinematic characteristics of the lower extremity during a simulated skiing exercise in healthy participants Hyo Taek Lee, PhD1), Hyo Lyun Roh, PhD, PT2), Yoon Sang K im, PhD1)* 1) Human-centered
Interaction Laboratory, Department of Computer Science and Engineering, Korea University of Technology and Education: 1600 Chunjeol-ro, Cheonan 330-708, Republic of Korea 2) Department of Physical Therapy, Kangwon National University, Republic of Korea
Abstract. [Purpose] Currently, various simulators are produced and used for athlete’s exercise, rehabilitation, and training. In this study, we analyzed the kinematic factors of sectional and total movements in healthy participants by providing group-dependent information during simulated exercise. [Subjects and Methods] Participants in this study included 26 male adults (non-experts and experts); experts held a certificate issued by the Korea Ski Instructors Association. The elapsed times in each phase, the difference in the lower extremity angles, and muscle activity were computed through analysis of kinematic factors. [Results] We observed that motions in the experts took shorter time to perform than that in non-experts, and showed larger variation of lower limb joint angle in most events during simulated skiing. There were also significant group-dependent differences in the peak and mean EMG values during simulated skiing. [Conclusion] A non-expert’s posture leads to enhanced muscle activity to keep the lower body in balance. We suggest the following training guideline: initially, non-experts should maintain appropriate range of motion with lower-intensity exercise to improve muscle endurance. It can be useful in providing preliminary data for future training and rehabilitation studies, as well as improvements in muscle strength and balance. Key words: Lower extremity, Muscle activity, Simulated skiing (This article was submitted Sep. 30, 2015, and was accepted Nov. 19, 2015)
INTRODUCTION High postural performance in normal and challenging conditions is crucial not only for many daily activities but also for attaining excellence and avoiding injuries in many sports. In alpine skiing especially, postural exercises are introduced in the training programs of expert skiers. Training for postural control can improve postural performance in healthy and non-injured athletes1). Specific indoor balance training is beneficial in learning alpine skiing2) and muscle strength training for ordinary people who have restrictions on outdoor activities. To allow people to enjoy sports such as skiing conveniently in a confined space during non-winter seasons, ski activities, through a ski simulator, provide a way to efficiently practice ski-turn motor performance4). Recently, research has focused on the development of ski simulators and exercise using them. Additionally, studies on the utilization of ski simulators to prevent injury are in progress. Lee et al.3) reported that training with a ski simulator contributes to body movement as well as strengthening the hamstrings, which helps prevent sports-related injuries. Muller et al.4) developed a specific training device called ski power simulator based on a kinetic and kinematic analysis of alpine slalom performed by senior and junior Austrian national teams. Panizzolo et al.5) have focused on 3-D motion analysis systems with reflective surface markers. In addition to this, Panizzolo et al.5) have used ski simulators to study equipment
*Corresponding author. Yoon Sang Kim (E-mail: [email protected]) ©2016 The Society of Physical Therapy Science. Published by IPEC Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License .
characteristics and exercise methods and compared their measurements with those measured in similar conditions while skiing on snow. Results of their study suggest that the two devices (skier’s edge and skimagic treadmill) are not effectively applicable for strength training, but the pattern of EMG activity was closer to real snow skiing. Currently, various simulator products are produced by many organizations, and ski simulators are used for personal and public rehabilitation as well as training. Furthermore, differences between individuals exist depending on the level of ski technique and the type of simulator. If the purpose of the ski simulator is to provide effects similar to actual skiing, it should encourage accurate posture and provide kinematic information not only for athletes but also for many daily and recreational skiers. At a time when Korea and China are preparing to host the 2018 and 2022 Winter Olympic Games, interest in skiing is growing. In addition to an increase in the skiing population in Asian countries, the rapid increase in the absolute number of injuries incurred while skiing has made the sport a major issue of interest in the area of sport injuries3). Being able to easily partake in a sports activity such as skiing in a confined area is thought to be useful in providing preliminary data for future training and rehabilitation studies, as well as improvements in muscle strength and balance. The primary aim of this study was to capture the kinematic characteristics in lower extremity range of motion (ROM) between two groups (non-expert and expert) and the effect of lower limb muscle activity during ski simulator exercise. A secondary aim of this study was to use the information collected to help maintain a safer and more accurate posture and efficiently use the joint’s range of motion and agonistic muscles in a ski simulator exercise.
SUBJECTS AND METHODS Twenty-six male adults (non-expert 13, expert 13) with no previous/current foot injury participated in this study. Subjects with any pathologies that could have affected the research and its results were excluded from the study6). Expert participants held a certificate issued by the Korea Ski Instructors Association and knew how to use a ski simulator. The study sample size was calculated using G*Power Version 3.17). With a 0.05 significance level and 0.8 power, a sample size of 12 participants per groups was calculated. The experiment was approved by the Pukyong National University Institutional Review Board. This experiment was conducted after its purpose and methods were sufficiently explained to the participants and they had consented to participate in this experiment. This experiment was conducted at a motion analysis laboratory in B City. The ski simulators (Pro ski simulator; Slovenia) were fixed onto a flat surface. For accurate results, sufficient time was given to each subject to practice, and then, the actual skiing footage was filmed for 30 seconds. Subjects wore tight pants in order to reduce discomfort and errors while collecting data. They held a pole in each hand; the middle intensities in the ski simulator were used in this study. In order to analyze the kinematic data dependent on each group, 5 infrared cameras (Visol, Korea) were used to record the participants’ performance of each movement. Motion data were sampled at 200 Hz. In order to set the reference frame, a calibration was conducted using a control box and surface markers were placed on 19 areas of the lower limbs to obtain the location coordinates of segments and joints (Table 1). Prior to testing, a relaxed standing calibration trial was captured first. EMG signals of 4 muscles of interest were selected on both legs, and skin preparation and electrode placement over the intended muscles were performed in accordance with the SENIAM8) concerted protocol. In order to prevent the noise due to extension lead between the surface electrodes and measuring instrument, the extension lead was fixed using tape. We measured the maximum voluntary isometric contraction (MVIC) of the individual muscles and the position of the surface electrode was attached to the rectus femoris, biceps femoris, tibialis anterior and gastrocnemius. The sampling frequency was established at 1,024 Hz. Motion analysis (Visol; Korea) and electromyography analysis (Laxtha; Korea) programs were used for data processing. For the data, the 41 control point system applied in calibration was used. The actual spatial coordinates were obtained and 3D coordinates of the human body were computed. The model of the human body was defined as a rigid body system. To remove digitizing errors and noise, the source data was filtered through the butterworth low pass filter. The cut-off frequency was set to 6 Hz and the 2D coordinates obtained from each digital camcorder were confirmed through the interpolation by cubic spline function. 3D coordinate calculations were analyzed using the DLT (Direct Linear Translation) method in kwon3d software. The computation of data were categorized into kinematic factors regarding muscle activities and then analyzed. The electromyography data were filtered through a 10 Hz high pass filter using Telescan (Laxtha) to minimize the noise afterwards. The obtained root mean square (RMS) value was standardized into a MVIC value and the %MVIC value was calculated. For the analysis of motion on the ski simulator, the points in time when the participants were at the center, when they were at the right peak and when they returned to the center were defined as Event 1, Event 2, and Event 3, respectively (Fig. 1). For an easy analysis of the movement in ski simulators, the signal recorded on the video data was used to set the analysis segments. In the event that the video lasted over 30 seconds, the best movement was chosen and classified into 3 events and 2 phases. Using these methods, the elapsed times in each phase, the difference in the lower extremity angles, and muscle activity were computed through analysis of kinematic factors. Participants’ performance was statistically analyzed using SPSS version 23.0. An independent samples t-test was conducted to examine changes in the lower joint motions, elapsed times, and muscle activity between the two groups. Differences were considered statistically significant at a p-value of less than 0.05.
627
Table 1. Surface markers of the lower limb joints Body model 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Utilized data Mid posterior superior iliac spine Right anterior superior iliac spine Left anterior superior iliac spine Right lateral thigh Left lateral thigh Right medial epicondyle Left medial epicondyle Right lateral epicondyle Left lateral epicondyle Right lateral shank Left lateral shank Right lateral malleolus Left lateral malleolus Right medial malleolus Left medial malleolus Right heel Left heel Right toe Left toe
Fig. 1. Event and phase of simulator exercise
RESULTS Observation of the elapsed times in each phase showed that non-experts spent 0.41 seconds in phase 1 and 0.40 seconds in phase 2. In the expert group, 0.35 seconds and 0.38 seconds were spent in phase 1 and 2, respectively. The total time spent was 0.82 seconds for non-experts, which was significantly longer than the 0.73 seconds observed in the experts (p
